主题：私募与量化投资讨论
问题：如何看待《全球最大基金用人工智能炒股试验，回报率超500%》这个新闻？
回答：先让我补充个原文链接 World’s Largest Fund Manager Is Replacing Its Employees With AIWorld’s Largest Fund Manager Is Replacing Its Employees With AIFor BlackRock Inc., the world's largest money management firm, using AI in lieu of human money managers might make stock picking cheaper and more profitable.A couple of weeks back, a study conducted in Germany revealed how AI, using deep-learning techniques, was able to lord it over the stock market by earning returns in the range of 500% – 700% in times of financial turmoil, and higher than actual market returns by about 30% during relatively stable times. This information must have given some stock traders a bit of a scare because it virtually showed that their jobs might be one of those that can be displaced by AI too. As it turns out, the apprehension they are feeling has reasonable basis. And the world’s biggest fund manager has just made a giant step towards this direction.We’re referring to BlackRock Inc. (NYSE:BLK) and their announcement that they would be cutting more than 40 jobs and replacing some of the firm’s human portfolio managers with AI. And it’s all part of their plan to transition to automated solutions through reorganization and more emphasis on algorithm-based investment actions, reports The New York Times.On the business side, the move seems a sensible one because returns gained don’t always justify the high cost of employing and retaining money managers. As it is, investors have started to doubt whether paying high fees to have their accounts managed is really worth it, especially because investment advisors typically rely on market indicators and trends, something that an AI programmed with sufficient algorithms can do all too well.In a recent report by research firm Opimas, they said they are expecting financial companies to spend over $1.5 billion on AI-related technologies. By 2021, that spending will increase to $2.8 billion. And by 2025, these firms will realize a significant improvement in their cost-to-income ratio, and it will be at the expense of a reduction in their workforce with computers taking the place of their human workers.On the personal side, of course it’s a bit disconcerting because there’s clearer indication that AI might indeed be after our jobs. And not just manual or blue collar jobs because that’s actually pretty understandable.Now that a major finance company has shown that it is confident enough to replace money managers, replacing other jobs that might be performed better by AI which rely solely on data might not be far behind. And that’s what’s truly scary since it implies that any profession that is knowledge and information based may be vulnerable to automation.While there are still many instances when customers prefer human interaction, it’s not hard to see that technology is slowly changing this aspect too. Think automated ordering, ticket booking, checkout counters, even complaint filing through those chatbots. Although it felt a bit unnatural when all that automation started, it doesn’t feel as unnatural now that we’re getting used to it, right? I mean, let’s be realistic here. Technology has made our lives way easier and way more advanced. Sure, automation sounds a bit scary when it comes to job-elimination, but universal basic income is certainly there as a viable option ; always, if we would be smart enough to adopt it. All it takes is for one country to implement it and eventually everyone else will start following the model. But getting back to the BlackRock Inc story and the case of stock-pickers and stock market traders getting replaced, it’s probably too early to conclude that they’ll be out of jobs soon. But the threat is real. That much we can be certain of.500%这个数字应该出自这里：AI Annihilates The Stock Market Achieving Eye-Popping Returns, Study ShowsUsing deep learning techniques, AI algorithms were able to earn returns that would have made you richer had the transactions been real.Based on the results of a study conducted by an international team of researchers at the School of Business and Economics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), AI-based algorithms can function as stock market traders. And they’re not just good at it. They’re actually much better than real live traders (man, that hurts). And they seemed to do particularly well during times of financial turmoil.To arrive at these results, the team –headed by Dr. Christopher Krauss of the Chair for Statistics and Econometrics at FAU — studied the S&P 500 Index which basically consists of the top 500 US stocks. For the period from 1992 to 2015, they used different methods, specifically, ‘deep learning, gradient boosting, and random forests’, to generate daily predictions for each of the 500 stocks.As different as the methods were, the models they trained learned one particular complex function — to define the relationship between features that were price-based and the future performance of the different stocks. Armed with this knowledge, the models were able to perform astonishingly well in the market.From the year 2000, the returns earned were higher than actual market returns by 30% per annum. In the nineties, the returns were even higher. And notably, the models did extra well at times when the financial market was most unstable.For instance, when the dot-com bubble collapsed in 2000, returns using the AI method were over 500%. And during the world financial crisis in 2008 – 2009, the AI systems showed returns of almost 700%. Well, this is not beating the market, it’s more like annihilating it.As Dr. Krauss said in a statement they issued: “Our quantitative algorithms have turned out to be particularly effective at such times of high volatility, when emotions dominate the markets.”In other words, when humans tend to lose focus and make decisions based on emotions rather than reason, AI remains on track because they only consider actual data and do not have any emotions that can get in the way of making sound judgements.While there is much benefit that can be gained from using AI in the financial market sector, Dr. Krauss cautions that it is ‘not necessarily the ‘Holy Grail of capital market trading’. As he pointed out, earnings declined in the latter years of the study, and it’s probably because machine learning and computer-based trading were becoming more common then. Meaning, it was becoming harder to be at an advantage by using computational power because the technique has become more widespread and was no longer being used by a limited few.Still, the outcome of the study proves without doubt how much wider the applications of deep learning are, especially in cases when human emotions tend to affect actions and decisions negatively.Dr. Krauss and his team are now working on follow-up projects that involve ‘larger data sets and very deep network architectures’ that will hopefully result in even better forecasting models.The results of the study have been published in the European Journal of Operational Research under the title “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500”.再早些的信息来自这里:Deep learning and stock tradingDeep learning and stock tradingUNIVERSITY OF ERLANGEN-NUREMBERGIn their study, researchers of the School of Business and Economics have shown that algorithms based on artificial intelligence are able to make profitable investment decisions. When applied to the S&P 500 constituents from 1992 to 2015, their stock selections generated annual returns in the double digits -- whereas the highest profits were made at times of financial turmoil. The findings have recently been published in the European Journal of Operational Research (EJOR) -- a leading outlet in the field of operational research and decision making.In March 2016, Lee Sedol -- one of the best human players in the Asian board game Go - lost against AlphaGo, a software developed by Google DeepMind. Compared to chess, Go is much more complex, and has long been considered an Everest for artificial intelligence research. Driving force for such achievements are computer programs that are loosely inspired by how biological brains work, i.e., by learning from examples and independently extracting relationships from millions of data points relationships from millions of data points. 'Artificial neural networks are primarily applied to problems, where solutions cannot be formulated with explicit rules,' explains Dr. Christopher Krauss of the Chair for Statistics and Econometrics at FAU. 'Image and speech recognition are typical fields of application, such as Apple's Siri. But the relevance of deep learning also increases in other domains, such as weather forecasting or the prediction of economic developments.'Analysing capital market dataThe international team around Dr. Christopher Krauss -- consisting of Xuan Anh Do (FAU) and Nicolas Huck (ICN Business School, France) -- were the first researchers to apply a selection of state-of-the-art techniques of artificial intelligence research to a large-scale set of capital market data. 'Equity markets exhibit complex, often non-linear dependencies,' says Krauss. 'However, when it comes to selecting stocks, established methods are mainly modelling simple relationships. For example, the momentum effect only focuses on a stock's return over the past months and assumes a continuation of that performance in the months to come. We saw potential for improvements.' To find out whether automated learning approaches perform better than a naïve buy-and-hold strategy, researchers studied the S&P 500 Index, which consists of the 500 leading US stocks. For the period from 1992 to 2015, they generated predictions for each individual stock for every single trading day, leveraging deep learning, gradient boosting, and random forests.Outperformance with machine learningEach of these methods was trained with approximately 180 million data points. In the course of this training, the models learned a complex function, describing the relationship between price-based features and a stock's future performance. The results were astonishing: 'Since the year 2000, we observed statistically and economically significant returns of more than 30% per annum. In the nineties, results were even higher, reflecting a time when our machine learning approaches had not yet been invented,' adds Krauss. These results pose a serious challenge to the efficient-market hypothesis. Returns were particularly high during times of financial turmoil, e.g., the collapse of the dot-com bubble around the year 2000 or the global financial crisis in 2008/2009. Dr Krauss: 'Our quantitative algorithms have turned out to be particularly effective at such times of high volatility, when emotions are dominating the markets.'Deep learning still has potentialHowever, Dr. Krauss lowers expectations: "During the last years of our sample period, profitability decreased and even became negative at times. We assume that this decline was driven by the rising influence of artificial intelligence in modern trading - enabled by increasing computing power as well as by the popularization of machine learning." However, the researchers agree that deep learning still has significant potential: 'Currently, we are working on... working on very promising follow-up projects with far larger data sets and very deep network architectures which have been specifically designed for identifying temporal dependencies,' says Krauss. '"First results already show significant improvements of predictional accuracy - also in recent years." - also in recent years.'The results of the study were published under the title "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500" in the European Journal of Operational Research.下面让我们再看看最原始的文件:Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&amp;amp;amp;amp;amp;amp;amp;amp;P 500恩，就是这篇了。